Expense Data Analysis using Python & SQL

📌 Project Overview

This project focuses on analyzing transactional expense data to extract meaningful insights. It demonstrates a step-by-step evolution from basic CSV-based analysis to SQL-powered analytics using Python.

🧱 Tech Stack

• Python
• Pandas
• SQLite
• Matplotlib
• SQL

📁 Project Structure

  expense-data-analysis/
  │
  ├── expenses.csv # Source dataset
  ├── dashboard.py # streamlit dashboard UI
  ├── load_to_db.py # Loads CSV data into SQLite database
  ├── analysis.py # Executes SQL queries and generates charts
  ├── sql_queries.sql # SQL queries for analytics
  ├── requirements.txt
  ├── README.md
  └── .gitignore

🚀 Level 1: CSV-Based Data Analysis (Basic)

Description

In Level 1, expense data is analyzed directly from a CSV file using Python and Pandas. The goal is to understand the dataset and perform basic aggregations and visualizations.

Features

• Load CSV data using Pandas
• Category-wise expense aggregation
• Monthly spending trend analysis
• City-wise and payment-method analysis
• Data visualization using Matplotlib

Skills Demonstrated

• Data cleaning and preprocessing
• Pandas groupby operations
• Basic data visualization
• Python scripting

▶️ How to run only this level-1

1. Comment out the level-2 part in analysis.py
2. Uncomment the level-1 part in analysis.py
3. Install dependencies

   pip install -r requirements.txt

4. Run in terminal

   python analysis.py

🚀 Level 2: SQL-Based Data Analysis (Expansion)

Description

In Level 2, the project is expanded by introducing an SQL database. The CSV data is loaded into an SQLite database, and all analytics are performed using SQL queries. Python is used to execute queries and visualize results.

Features

• Load CSV data into SQLite3 database
• Write reusable SQL queries for analytics
• Execute SQL queries using Python
• Generate charts from SQL query results

SQL Queries Implemented

• Category-wise total spending
• Monthly spending trend
• City-wise spending distribution
• Payment method usage analysis

Skills Demonstrated

• SQL aggregation and grouping
• SQLite database handling
• Python–SQL integration
• Data-driven visualization
• Clean project architecture

📊 Visualizations

• Bar chart for category-wise spending and payment method analysis
• Line chart for monthly spending trends
• Pie chart for city-wise spending

▶️ How to Run

1. Install dependencies

   pip install -r requirements.txt

2. Load CSV data into database

   python load_to_db.py

3. Run analysis and generate charts

   python analysis.py

🚀 Level 3: Interactive Dashboard

Description

An interactive dashboard built using Streamlit to visualize SQL-based expense analytics. The dashboard presents category-wise, monthly, city-wise and payment method spending insights in a user-friendly web interface.

Features

• Interactive web dashboard
• Charts generated from SQL query results
• Clean and minimal UI

▶️ How to Run

1. Install dependencies

   pip install -r requirements.txt

2. Run the following command

   streamlit run dashboard.py

3. You'll be redirected to local url in your web browser